Towards Transformational Creation of Novel Songs

We study transformational computational creativity in the context of writing songs and describe an implemented system that is able to modify its own goals and operation. With this, we contribute to three aspects of computational creativity and song generation: (1) Application-wise, songs are an interesting and challenging target for creativity, as they require the production of complementary music and lyrics. (2) Technically, we approach the problem of creativity and song generation using constraint programming. We show how constraints can be used declaratively to define a search space of songs so that a standard constraint solver can then be used to generate songs. (3) Conceptually, we describe a concrete architecture for transformational creativity where the creative (song writing) system has some responsibility for setting its own search space and goals. In the proposed architecture, a meta-level control component does this transparently by manipulating the constraints at runtime based on self-reflection of the system. Empirical experiments suggest the system is able to create songs according to its own taste.Peer reviewe

Automatical Composition of Lyrical Songs

Peer reviewe

ReLyMe: Improving Lyric-to-Melody Generation by Incorporating Lyric-Melody Relationships

Lyric-to-melody generation, which generates melody according to given lyrics, is one of the most important automatic music composition tasks. With the rapid development of deep learning, previous works address this task with end-to-end neural network models. However, deep learning models cannot well capture the strict but subtle relationships between lyrics and melodies, which compromises the harmony between lyrics and generated melodies. In this paper, we propose ReLyMe, a method that incorporates Relationships between Lyrics and Melodies from music theory to ensure the harmony between lyrics and melodies. Specifically, we first introduce several principles that lyrics and melodies should follow in terms of tone, rhythm, and structure relationships. These principles are then integrated into neural network lyric-to-melody models by adding corresponding constraints during the decoding process to improve the harmony between lyrics and melodies. We use a series of objective and subjective metrics to evaluate the generated melodies. Experiments on both English and Chinese song datasets show the effectiveness of ReLyMe, demonstrating the superiority of incorporating lyric-melody relationships from the music domain into neural lyric-to-melody generation.Comment: Accepted by ACMMM 2022, ora

Procedural Generation of Musical Metrics Based on Lyrics Analysis

Mais do que a componente semântica e discursiva, as letras musicais contêm geralmente outro tipo de informação, que mais do que com o ato da escrita, tem que ver com o ato da pronúncia. Assumindo que uma letra musical é escrita para posteriormente ser reproduzida verbalmente, há um cuidado para que esse processo nos transmita algo também, completamente diferente daquilo que nos é transmitido pela letra no papel. A sincronia das acentuações fonéticas e lexicais da letra com as componentes musicais em que se insere é disso o maior exemplo. Neste projeto, a proposta é criar um sistema capaz de devolver informação musical para uma dada letra. Mais concretamente, informação relativa à métrica. Para o efeito, utilizarei o CMUdict, um dicionário de informação fonética para a língua inglesa que contém, para cada palavra, a divisão por fonemas com os respectivos marcadores referentes à sua acentuação. Todo o funcionamento do sistema será baseado na linguagem de programação Python, tendo sido todo o código desenvolvido por mim especialmente para o projeto. Para cada letra introduzida, será executada uma análise por versos e cada verso será transformado num template métrico. Todos os versos da letra serão ajustados a cada um dos templates e serão classificados, de forma a perceber-se qual o template que melhor se ajusta à letra em geral. O template com maior pontuação será escolhido como estrutura métrica final.More than the semantic and discursive components, the musical lyrics often contain other information, that more than with the act of writing, has to do with the act of pronunciation. Assuming that the musical lyrics are written to later be reproduced verbally, there is a caution for this process to pass us something too, completely different from what is conveyed by the lyrics on paper. The synchrony of phonetic and lexical accents of the lyrics with the musical components in which it belongs is a great example of that. In this project, the proposal is to create a system able to return music information for a given lyrics. More specifically, information on the metrics. To this end, I will use the CMUdict, a phonetic information dictionary for English language that contains, for each word, the division of its phonemes with the respective markers related to their stress. The entire operation of the system will be based on Python programming language, having all the code been developed by me especially for the project. For each letter entered, it will run an analysis by verses and each verse will become a metric template. All the verses from the lyrics will be adjusted to each of the templates and will be classified in order to select what is the template that best fits the letter in general. The template with the highest score will be chosen as the metric final structure

Musical Sytle in Selected Easter Cantatas by John W. Peterson

John W. Peterson is one of the most celebrated American Christian songwriters between 1950s and 1970. He is the best-known for church cantatas such as Christimas, Easter, General, and Choral collections. Peterson\u27s life, music style, and success will be addressed. Next, four Easter cantatas of his cantatas will be analyzed and compared: No Greater Love, The Glory of Easter, Hallelujah for the Cross, and It Took a Miracle. Specifically, there are introduction, his biographical information, historical and musical background in 1950s and 1960s, introduction of four Easter cantatas, analysis of four Easter cantatas, that is, music styles of four Easter cantatas, and conclusion. Choirs and church cantatas play a significant role in evangelizing unbelievers and edifying Christians. However, choirs and church cantatas are disappearing more and more today. Therefore, analyzing music styles of Peterson\u27s four Easter cantatas will not only be helpful for preaching the gosple to non-believers and improving Christians spiritually, but also importantly influencing future Christian musicians and worship leaders, church music, and church ministry

AUTOMATED LYRICAL NARRATIVE WRITING

Computational Creativity studies the potential of computers to act as autonomous creators and co-creators in addition to tools helping people. Creativity is evident in music, visual art, problem solving and languages. Significant work has been conducted in the area of linguistic creation mainly in the generation of stories, puns, rhymes, jokes, similes, and poetry. One of the major challenges of computational creativity is to generate lyrics that exhibit human-level creativity. On one hand, the lyrics generated should be meaningful and coherent, while on the other hand, they should satisfy poetry constraints such as rhyme scheme, rhyme type, and the number of syllables. The goal of this project is to combine the two art forms of storytelling and lyrics writing through the automated creation of coherent lyrics. The project also highlights the approaches to the creation of poetry and lyrics. The resulting model is named MexicA’s BaLlad MachinE (MABLE). This is the first computational system that generates narrative-based lyrics

Implementing an Intelligent Collaborative Agent as Teammate in Collaborative Writing: toward a Synergy of Humans and AI

This paper aims at implementing a hybrid form of group work through the incorporation of an intelligent collaborative agent into a Collaborative Writing process. With that it contributes to the overall research gap establishing acceptance of AI towards complementary hybrid work. To approach this aim, we follow a Design Science Research process. We identify requirements for the agent to be considered a teammate based on expert interviews in the light of Social Response Theory and the concept of the Uncanny Valley. Next, we derive design principles for the implementation of an agent as teammate from the collected requirements. For the evaluation of the design principles and the human teammates’ perception of the agent, we instantiate a Collaborative Writing process via a web-application incorporating the agent. The evaluation reveals the partly successful implementation of the developed design principles. Additionally, the results show the potential of hybrid collaboration teams accepting non-human teammates